In the Antarctic dataset, GDGT-I, GDGT-Ib, and GDGT-Ic all showed a weak negative correlation with RJR-2403 (r=−0.33r=−0.33, −0.30, −0.46, all p≤0.1p≤0.1, respectively). GDGT-IIIb showed no significant relationship with pH (r=0.16r=0.16, p=0.355p=0.355), conductivity or water depth. Similarly to Pearson et al. (2011), we found that although GDGT-Ib showed a weak correlation with pH, the residuals of the new Antarctic and sub-Antarctic GDGT–temperature calibration model were not significantly correlated to pH (nor to conductivity or water depth). We therefore conclude that our calibration is not confounded by these variables.
Similar relationships between brGDGT compounds and temperature in this research have been identified in previous studies. GDGT-I and GDGT-Ic dominated in warmer environments (Pearson et al., 2011, Loomis et al., 2012 and Woltering et al., 2014), whereas in colder environments, such as the Arctic (Shanahan et al., 2013 and Peterse et al., 2014), Antarctic and Siberia (Pearson et al., 2011), and the high mountains of the Tibetan Plateau (Günther et al., 2014), GDGT-II and/or GDGT-III dominated over GDGT-I. As expected, GDGT-III or GDGT-II was dominant in every sample in the Antarctic and sub-Antarctic dataset.
Similar relationships between brGDGT compounds and temperature in this research have been identified in previous studies. GDGT-I and GDGT-Ic dominated in warmer environments (Pearson et al., 2011, Loomis et al., 2012 and Woltering et al., 2014), whereas in colder environments, such as the Arctic (Shanahan et al., 2013 and Peterse et al., 2014), Antarctic and Siberia (Pearson et al., 2011), and the high mountains of the Tibetan Plateau (Günther et al., 2014), GDGT-II and/or GDGT-III dominated over GDGT-I. As expected, GDGT-III or GDGT-II was dominant in every sample in the Antarctic and sub-Antarctic dataset.